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Shortest-Path-based Back-Pressure Routing with Single FIFO Queueing in Ad hoc Networkshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5085
2019-02-12T00:00:00ZCAMD:A Switch Migration Based Load Balancing Framework for Software Defined Networkshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5166
2019-02-12T00:00:00ZCoverage and Capacity Improvement of Millimetre Wave 5G Network Using Distributed Base Station Architecturehttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5059
2019-02-11T00:00:00ZAnalysis of Traffic Offload using Multi Attribute Decision Making Technique in Heterogeneous Shared Networkshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5139
2019-02-04T00:00:00ZDistributed Routing Algorithm with Dynamic Connection Partition for Mobile Ad Hoc Networkshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5150
2019-01-18T00:00:00ZRandom access networks with separable schemeshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5086
<p>Consider a time slotted multiuser random access network where <i>M</i> users have access to a single channel and each user attempts to send packets in a particular time slot with a certain probability, according to a predetermined access scheme. This forms an interacting system of queues and in this study, the authors consider generic access schemes that satisfy a separability condition and obtain bounds for the stability region, directly using a Lyapunov-type analysis. They apply their results to the case of random access networks with partial conflicts where any particular user has collision conflicts with only a subset of the other users. Using conflict graphs to quantify the degree of such conflicts, they obtain bounds for the stability region for both deterministic and random conflict graphs that model, respectively, static and mobile networks.</p>2019-01-04T00:00:00ZCluster-based distributed cooperative spectrum sensing over Nakagami fading using diversity receptionhttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5002
<p>Cooperative spectrum sensing (CSS) can solve the problem of hidden terminal in cognitive radio (CR). CSS can be employed in two ways distributed and centralised. In wireless networking, clustering can provide network scalability, better resource allocation, and energy efficiency. Here, cluster-based distributed CSS is investigated over Nakagami channel. In the proposed scheme, four fusion policies OR–OR, OR–AND, AND–OR, and AND–AND are investigated. An analytical framework has been presented to evaluate different parameters related to spectrum sensing, i.e. detection probability, false alarm probability, and missed detection probability for Nakagami fading channel. On the basis of the developed framework, the performance of cluster-based distributed CSS has been compared with the centralised CSS. Results show that OR–OR fusion rule of the cluster-based distributed CSS outperforms other fusion rules and centralised CSS. To achieve 80% probability of detection, SNR required is <10 dB in OR–OR fusion, whereas it is >10 dB for other fusion schemes. Further performance improvement is achieved by using square law selection and maximum ratio combining diversity schemes.</p>2019-01-02T00:00:00ZVolume 8, Issue 1https://digital-library.theiet.org/content/journals/iet-net/8/1
2019-01-01T00:00:00ZSingle CCA for IEEE 802.15.4 networks: a cross layer energy modelhttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5108
<p>For data frame transmissions in beacon-enabled mode, an IEEE 802.15.4 node performs two consecutive clear channel assessments (CCAs) to sense the channel after back off. Here, the authors study what would happen if the medium access control (MAC) layer performs only one CCA instead. Although it is apparent that the frame transmission cycle would be shortened with single CCA, it is not straightforward to assess the effect of single CCA on the overall transmission energy. A Markov chain-based MAC operation model is therefore constructed to analyse single-CCA-based MAC layer parameters. A combined physical (PHY) and MAC layer energy model is formulated next. It was found that the single CCA-based MAC outperforms its standard double CCA-based counterpart from the energy efficiency perspective.</p>2018-12-11T00:00:00ZDeep learning approach to multimedia traffic classification based on QoS characteristicshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5179
<p>With the fast increase of multimedia traffic in Internet of Things (IoT) applications, IoT traffic now requires very different Quality of Service (QoS). By extensive statistical analysis of traffic flow data from a real world network, the authors find that there are some latent features hidden in the multimedia data, which can be useful for accurately differentiating multimedia traffic flows from the QoS perspective. Under limited training data conditions, existing shallow classification methods are limited in performance, and are thus not effective in classifying emerging multimedia traffic types, which have truly entered the era of big data and become very completed in QoS features. This situation inspires us to revisit the multimedia traffic classification problem with a deep learning (DL) approach. In this study, an improved DL-based multimedia traffic classification method is proposed, which considers the inherent structure of QoS features in multimedia data. An improved stacked autoencoder model is employed to learn the relevant QoS features of multimedia traffic. Extensive experimental studies with multimedia datasets captured from a campus network demonstrate the effectiveness of the proposed method over six benchmark schemes.</p>2018-12-11T00:00:00ZAItalk: a tutorial to implement AI as IoT deviceshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5182
<p>In one of the recent trends of Internet of Things (IoT), the IoT data are manipulated by Artificial Intelligence (AI) techniques for smart applications. By including AI into existing IoT application programs, significant coding effort is required. This paper proposes a solution called AItalk to resolve this issue. Unlike traditional AI-based IoT applications that tightly integrate the AI mechanism within the network applications, the novel idea of AItalk is to treat the machine learning mechanism as a cyber IoT device. Our solution allows seamless inclusion of machine learning capability to the existing IoT applications without any programming effort. The advantage of this approach is that we can decompose a complex AI application into simplified distributed modules connected by using the IoT technology, and therefore the AI solution can be built more effectively. Also, in our approach, data can be easily processed in real time for an AI application. Supervised machine learning naturally fits the IoT applications, where the sensors provide the features to the AI algorithms, and the remote controllers serve as the labels. We show an example that the overhead of the IoT communication in AItalk is less than 30 ms and the AI prediction time is less than 2 ms.</p>2018-12-07T00:00:00ZChannel estimation for massive multiple-input and multiple-output system based on different measurement matriceshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5198
<p>This study proposes a construction method of deterministic measurement matrix based on a correlation criterion. On this basis, the authors investigated the massive multiple-input and multiple-output (MIMO) channel estimation algorithm using the complementary sequence as measurement matrix and obtained the compressed sensing (CS) signal model through the analysis on traditional massive MIMO channels. In this model, the measurement matrix plays the role of the pilot sequence in the traditional models. Then, the complementary sequence was determined as the measurement matrix for channel estimation. Meanwhile, the regularised orthogonal matching pursuit (ROMP), which realises accurate signal recovery through reselection of support set, was employed. Finally, the CS-based channel estimation model was analysed, and the channel estimation results of different measurement matrices were compared through simulations. The results show that the complementary sequence outperforms the other two sequences when serving as a measurement matrix; the ROMP-based channel estimation surpassed the orthogonal matching pursuit and matching pursuit algorithms in both computing accuracy and runtime.</p>2018-11-28T00:00:00ZVerification and Validation Techniques for Streaming Big Data Analytics in Internet ofThings Environmenthttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.518
2018-11-28T00:00:00ZFine-Tunning Restricted Boltzmann MachinesUsing Quaternions and its Application for SpamDetectionhttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.517
2018-11-23T00:00:00ZIntrusion detection system for detecting wireless attacks in IEEE 802.11 networkshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5050
<p>Sophisticated wireless attacks such as Wifiphishing, Evil twin and so on are a serious threat to Wi-Fi networks. These attacks are tricky enough to spoof users by launching a fake access point (AP) pretending to be a legitimate one. The existing intrusion detection schemes are prone to a high rate of false positives as they depend on restricted features. Hence, an efficient intrusion detection system, which considers many more features is needed. Kernel density estimation (KDE) statistically models the distribution of data and detects the attacks in active mode. However, in passive mode (without any connectivity to the AP), detecting the attack is complicated and it requires prior knowledge of attack signatures. The other intrusion detection model which is used in passive mode, namely hidden Markov model (HMM), does not need knowledge of initial probabilities. In this study, a novel wireless intrusion detection system is proposed, by combining KDE and HMM through a tandem queue with feedback. The proposed KDE-HMM technique/method combines the advantages of both statistical and probabilistic properties to yield better results. The performance of the proposed KDE-HMM technique has been experimentally validated and it is found that the proposed KDE-HMM detects the aforementioned attacks with 98% accuracy.</p>2018-11-21T00:00:00ZRandomised scheduling algorithm for virtual output queuing switch at the presence of non-uniform traffichttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5115
<p>Virtual Output Queuing (VOQ) is a well-known queuing discipline in data switch architecture that eliminates Head of Line (HOL) blocking issue. In VOQ scheme, for each output port, a separate FIFO is maintained by each input port. Consequently, a scheduling algorithm is required to determine the order of service to virtual queues at each time slot. Maximum Weight Matching (MWM) is a well-known scheduling algorithm that achieves the entire throughput region. Despite of outstanding attainable throughput, high complexity of MWM makes it an impractical algorithm for implementation in high-speed switches. To overcome this challenge, a number of randomised algorithms have been proposed in the literature. They commonly perform poorly when input traffic does not uniformly select output ports. Here, the authors propose two randomised algorithms that outperform the well-known formerly proposed solutions. They exploit a method to keep a parametric number of heavy edges from the last time matching and mix it by randomly generated matching to produce a new schedule. Simulation results confirm the superior performance of the proposed algorithms.</p>2018-11-12T00:00:00ZArtificial intelligence enabled software-defined networking: a comprehensive overviewhttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5082
<p>Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.</p>2018-11-12T00:00:00ZOpportunistic content dissemination in mobile social networks via adjustment of user selfishnesshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5013
<p>By increase in smart phone penetration rate, mobile social networks (MSNs) become more popular. In such networks, users can exchange and share information via peer-to-peer opportunistic wireless connections. Wireless connections are prone to failures, devices are battery-powered, and the buffer space is limited. These lead to uncertainty in connections and selfish behaviours in dissemination processes. Hence, information dissemination in MSNs becomes a challenge. In this study, the authors analyse the information dissemination in MSNs with selfish users from different communities. They develop an analytical model through ordinary differential equations to analyse the dissemination process in MSNs. Then, they propose an optimisation problem to find the optimal forwarding probabilities of users. They employ the branch and bound-outer approximation algorithm to analytically solve the optimisation problem. The analytical results represent that the optimal forwarding probability of users diminished by increasing the number of relay users, which accelerate the dissemination in the network. Also, these results represent that the proposed algorithm to find the optimal selfishness vector can improve the network performance by decreasing the dissemination delay.</p>2018-11-05T00:00:00ZVolume 7, Issue 6https://digital-library.theiet.org/content/journals/iet-net/7/6
2018-11-01T00:00:00ZDCnet: a data centre network architecture that supports live VM migrationhttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5094
<p>Cloud computing and storage have become an integral part of online Internet services. Over the past decade, enterprises have steadily moved their applications and storage to the cloud paradigm. Server virtualisation enables cloud providers to utilise computational resources efficiently by employing virtual machines (VMs) and containers. As more enterprise applications are hosted by VMs in the cloud, a significant amount of network communication occurs among servers within a data centre, increasing so-called ‘<i>east-west communication’</i>. In addition to scaling to accommodate more east-west traffic, future data centre networks must support efficient VM migration, which is used to optimise power consumption. This article proposes a new data centre network architecture called <i>DCnet</i>. DCnet changes addressing and routing at layers 2 and 3 completely to increase throughput and support live VM migration throughout an organisation, including across multiple data centres owned by an organisation that spans large geographic areas. DCnet retains compatibility with existing hardware by using the same frame format as standard protocols, specifically Ethernet and Internet protocol (IP). Furthermore, DCnet does not require any changes to applications and host operating systems or libraries. In addition to presenting the architecture, the article describes a test bed used to assess DCnet and reports experimental measurements.</p>2018-10-25T00:00:00ZPositioning optimisation based on particle quality prediction in wireless sensor networkshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5072
<p>The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non-linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high-quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.</p>2018-10-17T00:00:00ZWOAPR: an affinity propagation based clustering and optimal path selection for time-critical wireless sensor networkshttps://digital-library.theiet.org/content/journals/10.1049/iet-net.2018.5081
<p>Wireless sensor networks (WSNs) spread their returns to every field having the requirement of continuous monitoring. Clustering is an important mechanism in WSN for minimum energy consumption and better network performance. In these networks, optimal paths need to be determined for efficient flow of data. The power efficiency and quality of service are the two most essential aspects looked-for to realise the network. However, this hypothesis has been a changing characteristic based on the innovative applications. Consequently, the quality-of-service aspect is gaining much significance than the efficient power design of the sensor network. This study proposes a clustering-based routing protocol for time-critical WSN named as whale optimised affinity propagation-based routing protocol. This protocol reduces energy consumption and improves energy efficiency through clustering and routing algorithms. The performance of the proposed protocol is evaluated using various metrics and the proposed approach shows significant improvement over the existing approaches.</p>2018-10-01T00:00:00ZVolume 7, Issue 5https://digital-library.theiet.org/content/journals/iet-net/7/5
2018-09-01T00:00:00ZVolume 7, Issue 4https://digital-library.theiet.org/content/journals/iet-net/7/4
2018-07-01T00:00:00ZVolume 7, Issue 3https://digital-library.theiet.org/content/journals/iet-net/7/3
2018-05-01T00:00:00ZVolume 7, Issue 2https://digital-library.theiet.org/content/journals/iet-net/7/2
2018-03-01T00:00:00ZVolume 7, Issue 1https://digital-library.theiet.org/content/journals/iet-net/7/1
2018-01-01T00:00:00ZVolume 6, Issue 6https://digital-library.theiet.org/content/journals/iet-net/6/6
2017-11-01T00:00:00ZVolume 6, Issue 5https://digital-library.theiet.org/content/journals/iet-net/6/5
2017-09-01T00:00:00ZVolume 6, Issue 4https://digital-library.theiet.org/content/journals/iet-net/6/4
2017-07-01T00:00:00ZVolume 6, Issue 3https://digital-library.theiet.org/content/journals/iet-net/6/3
2017-05-01T00:00:00ZVolume 6, Issue 2https://digital-library.theiet.org/content/journals/iet-net/6/2
2017-03-01T00:00:00ZVolume 6, Issue 1https://digital-library.theiet.org/content/journals/iet-net/6/1
2017-01-01T00:00:00ZVolume 5, Issue 6https://digital-library.theiet.org/content/journals/iet-net/5/6
2016-11-01T00:00:00ZVolume 5, Issue 5https://digital-library.theiet.org/content/journals/iet-net/5/5
2016-09-01T00:00:00ZVolume 5, Issue 4https://digital-library.theiet.org/content/journals/iet-net/5/4
2016-07-01T00:00:00ZVolume 5, Issue 3https://digital-library.theiet.org/content/journals/iet-net/5/3
2016-05-01T00:00:00ZVolume 5, Issue 2https://digital-library.theiet.org/content/journals/iet-net/5/2
2016-03-01T00:00:00ZVolume 5, Issue 1https://digital-library.theiet.org/content/journals/iet-net/5/1
2016-01-01T00:00:00ZVolume 4, Issue 6https://digital-library.theiet.org/content/journals/iet-net/4/6
2015-11-01T00:00:00ZVolume 4, Issue 5https://digital-library.theiet.org/content/journals/iet-net/4/5
2015-09-01T00:00:00ZVolume 4, Issue 4https://digital-library.theiet.org/content/journals/iet-net/4/4
2015-07-01T00:00:00ZVolume 4, Issue 3https://digital-library.theiet.org/content/journals/iet-net/4/3
2015-05-01T00:00:00ZVolume 4, Issue 2https://digital-library.theiet.org/content/journals/iet-net/4/2
2015-03-01T00:00:00ZVolume 4, Issue 1https://digital-library.theiet.org/content/journals/iet-net/4/1
2015-01-01T00:00:00ZVolume 3, Issue 4https://digital-library.theiet.org/content/journals/iet-net/3/4
2014-12-01T00:00:00ZVolume 3, Issue 3https://digital-library.theiet.org/content/journals/iet-net/3/3
2014-09-01T00:00:00ZVolume 3, Issue 2https://digital-library.theiet.org/content/journals/iet-net/3/2
2014-06-01T00:00:00ZVolume 3, Issue 1https://digital-library.theiet.org/content/journals/iet-net/3/1
2014-03-01T00:00:00ZVolume 2, Issue 4https://digital-library.theiet.org/content/journals/iet-net/2/4
2013-12-01T00:00:00ZVolume 2, Issue 3https://digital-library.theiet.org/content/journals/iet-net/2/3
2013-09-01T00:00:00ZVolume 2, Issue 2https://digital-library.theiet.org/content/journals/iet-net/2/2
2013-06-01T00:00:00ZVolume 2, Issue 1https://digital-library.theiet.org/content/journals/iet-net/2/1
2013-03-01T00:00:00ZVolume 1, Issue 4https://digital-library.theiet.org/content/journals/iet-net/1/4
2012-12-01T00:00:00ZVolume 1, Issue 3https://digital-library.theiet.org/content/journals/iet-net/1/3
2012-09-01T00:00:00ZVolume 1, Issue 2https://digital-library.theiet.org/content/journals/iet-net/1/2
2012-06-01T00:00:00ZVolume 1, Issue 1https://digital-library.theiet.org/content/journals/iet-net/1/1
2012-03-01T00:00:00Z